Configure a private endpoint for an Azure Machine Learning workspace with SDK and CLI v1

APPLIES TO: Azure CLI ml extension v1 Python SDK azureml v1

In this document, you learn how to configure a private endpoint for your Azure Machine Learning workspace. For information on creating a virtual network for Azure Machine Learning, see Virtual network isolation and privacy overview.

Azure Private Link enables you to connect to your workspace using a private endpoint. The private endpoint is a set of private IP addresses within your virtual network. You can then limit access to your workspace to only occur over the private IP addresses. A private endpoint helps reduce the risk of data exfiltration. To learn more about private endpoints, see the Azure Private Link article.

Prerequisites

• You must have an existing virtual network to create the private endpoint in.
• Disable network policies for private endpoints before adding the private endpoint.

Limitations

• If you enable public access for a workspace secured with private endpoint and use Azure Machine Learning studio over the public internet, some features such as the designer may fail to access your data. This problem happens when the data is stored on a service that is secured behind the VNet. For example, an Azure Storage Account.

• You may encounter problems trying to access the private endpoint for your workspace if you're using Mozilla Firefox. This problem may be related to DNS over HTTPS in Mozilla Firefox. We recommend using Microsoft Edge or Google Chrome as a workaround.

• Using a private endpoint doesn't affect Azure control plane (management operations) such as deleting the workspace or managing compute resources. For example, creating, updating, or deleting a compute target. These operations are performed over the public Internet as normal. Data plane operations, such as using Azure Machine Learning studio, APIs (including published pipelines), or the SDK use the private endpoint.

• When creating a compute instance or compute cluster in a workspace with a private endpoint, the compute instance and compute cluster must be in the same Azure region as the workspace.

• When creating or attaching an Azure Kubernetes Service cluster to a workspace with a private endpoint, the cluster must be in the same region as the workspace.

• When using a workspace with multiple private endpoints, one of the private endpoints must be in the same VNet as the following dependency services:

  • Azure Storage Account that provides the default storage for the workspace
  • Azure Key Vault for the workspace
  • Azure Container Registry for the workspace.

  For example, one VNet ('services' VNet) would contain a private endpoint for the dependency services and the workspace. This configuration allows the workspace to communicate with the services. Another VNet ('clients') might only contain a private endpoint for the workspace, and be used only for communication between client development machines and the workspace.

Create a workspace that uses a private endpoint

Use one of the following methods to create a workspace with a private endpoint. Each of these methods requires an existing virtual network:

from azureml.core import Workspace
from azureml.core import PrivateEndPointConfig

pe = PrivateEndPointConfig(name='myprivateendpoint', vnet_name='myvnet', vnet_subnet_name='default')
ws = Workspace.create(name='myworkspace',
    subscription_id='<my-subscription-id>',
    resource_group='myresourcegroup',
    location='chinaeast2',
    private_endpoint_config=pe,
    private_endpoint_auto_approval=True,
    show_output=True)

az ml workspace create -r myresourcegroup \
    -l chinaeast2 \
    -n myworkspace \
    --pe-name myprivateendpoint \
    --pe-auto-approval \
    --pe-resource-group myresourcegroup \
    --pe-vnet-name myvnet \
    --pe-subnet-name mysubnet

Add a private endpoint to a workspace

Use one of the following methods to add a private endpoint to an existing workspace:

from azureml.core import Workspace
from azureml.core import PrivateEndPointConfig

pe = PrivateEndPointConfig(name='myprivateendpoint', vnet_name='myvnet', vnet_subnet_name='default')
ws = Workspace.from_config()
ws.add_private_endpoint(private_endpoint_config=pe, private_endpoint_auto_approval=True, show_output=True)

az ml workspace private-endpoint add -w myworkspace  --pe-name myprivateendpoint --pe-auto-approval --pe-vnet-name myvnet

Remove a private endpoint

You can remove one or all private endpoints for a workspace. Removing a private endpoint removes the workspace from the VNet that the endpoint was associated with. Removing the private endpoint may prevent the workspace from accessing resources in that VNet, or resources in the VNet from accessing the workspace. For example, if the VNet doesn't allow access to or from the public internet.

To remove a private endpoint, use the following information:

from azureml.core import Workspace

ws = Workspace.from_config()
# get the connection name
_, _, connection_name = ws.get_details()['privateEndpointConnections'][0]['id'].rpartition('/')
ws.delete_private_endpoint_connection(private_endpoint_connection_name=connection_name)

Enable public access

In some situations, you may want to allow someone to connect to your secured workspace over a public endpoint, instead of through the VNet. Or you may want to remove the workspace from the VNet and re-enable public access.

To enable public access, use the following steps:

from azureml.core import Workspace

ws = Workspace.from_config()
ws.update(allow_public_access_when_behind_vnet=True)

Securely connect to your workspace

To connect to a workspace that's secured behind a VNet, use one of the following methods:

• Azure VPN gateway - Connects on-premises networks to the VNet over a private connection. Connection is made over the public internet. There are two types of VPN gateways that you might use:

  • Point-to-site: Each client computer uses a VPN client to connect to the VNet.
  • Site-to-site: A VPN device connects the VNet to your on-premises network.

• ExpressRoute - Connects on-premises networks into the cloud over a private connection. Connection is made using a connectivity provider.

• Azure Bastion - In this scenario, you create an Azure Virtual Machine (sometimes called a jump box) inside the VNet. You then connect to the VM using Azure Bastion. Bastion allows you to connect to the VM using either an RDP or SSH session from your local web browser. You then use the jump box as your development environment. Since it is inside the VNet, it can directly access the workspace. For an example of using a jump box, see Tutorial: Create a secure workspace.

If you have problems connecting to the workspace, see Troubleshoot secure workspace connectivity.

Multiple private endpoints

Azure Machine Learning supports multiple private endpoints for a workspace. Multiple private endpoints are often used when you want to keep different environments separate. The following are some scenarios that are enabled by using multiple private endpoints:

• Client development environments in a separate VNet.

• An Azure Kubernetes Service (AKS) cluster in a separate VNet.

• Other Azure services in a separate VNet. For example, Azure Synapse and Azure Data Factory can use an Azure managed virtual network. In either case, a private endpoint for the workspace can be added to the managed VNet used by those services. For more information on using a managed virtual network with these services, see the following articles:

  • Synapse managed private endpoints.
  • Azure Data Factory managed virtual network.

  Important

  Synapse's data exfiltration protection is not supported with Azure Machine Learning.

Adding multiple private endpoints uses the same steps as described in the Add a private endpoint to a workspace section.

Scenario: Isolated clients

If you want to isolate the development clients, so they don't have direct access to the compute resources used by Azure Machine Learning, use the following steps:

1. Create another VNet for the clients. This VNet might contain Azure Virtual Machines that act as your clients, or it may contain a VPN Gateway used by on-premises clients to connect to the VNet.
2. Add a new private endpoint for the Azure Storage Account, Azure Key Vault, and Azure Container Registry used by your workspace. These private endpoints should exist in the client VNet.
3. If you have another storage that is used by your workspace, add a new private endpoint for that storage. The private endpoint should exist in the client VNet and have private DNS zone integration enabled.
4. Add a new private endpoint to your workspace. This private endpoint should exist in the client VNet and have private DNS zone integration enabled.
5. Use the steps in the Use studio in a virtual network article to enable studio to access the storage account(s).

The following diagram illustrates this configuration. The Workload VNet contains computes created by the workspace for training & deployment. The Client VNet contains clients or client ExpressRoute/VPN connections. Both VNets contain private endpoints for the workspace, Azure Storage Account, Azure Key Vault, and Azure Container Registry.

Scenario: Isolated Azure Kubernetes Service

If you want to create an isolated Azure Kubernetes Service used by the workspace, use the following steps:

1. Create an Azure Kubernetes Service instance. During creation, AKS creates a VNet that contains the AKS cluster.
2. Add a new private endpoint for the Azure Storage Account, Azure Key Vault, and Azure Container Registry used by your workspace. These private endpoints should exist in the client VNet.
3. If you have other storage that is used by your workspace, add a new private endpoint for that storage. The private endpoint should exist in the client VNet and have private DNS zone integration enabled.
4. Add a new private endpoint to your workspace. This private endpoint should exist in the client VNet and have private DNS zone integration enabled.
5. Attach the AKS cluster to the Azure Machine Learning workspace. For more information, see Create and attach an Azure Kubernetes Service cluster.

Next steps

• For more information on securing your Azure Machine Learning workspace, see the Virtual network isolation and privacy overview article.

• If you plan on using a custom DNS solution in your virtual network, see how to use a workspace with a custom DNS server.

• API platform network isolation